Crea.Blender: A Neural Network-Based Image Generation Game to Assess Creativity

Janet Rafner, Arthur Hjorth, Sebastian Risi, Lotte Philipsen, Charles Dumas, Michael Mose Biskjær, Lior Noy, Kristian Tylén, Carsten Bergenholtz, Jesse Lynch, Blanka Zana, Jacob Sherson

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players 'blend' existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.
Original languageEnglish
Title of host publicationExtended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Publication date2020
ISBN (Print)9781450375870
Publication statusPublished - 2020


  • co-creative systems
  • human-computer collaboration
  • creativity assessment
  • crea.blender
  • human creativity
  • machine learning in creativity
  • image blending
  • pilot study
  • player behavior
  • playful experience


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